Endogenous technical change and climate change

One of the central problems in understanding climate change is understanding how it will impact the economy. Most current models assume that climate change will reduce usable output. The resulting effects on long term growth under such an assumption are small. If usable output is reduced, there will likely be some reduction in savings and hence capital available in future periods. Nevertheless, the core drivers of growth such as technological development remain. As a result, under most models, the world is an order of magnitude or more richer in several hundred years even if temperature change is extreme and the damages from climate change potentially intolerable. Some models consider the possibility that climate change will directly destroy capital (i.e., such as through destruction of buildings in storms). While growth is somewhat slower in this formulation, the basic result is unchanged.

In this project we consider a simple modification to the standard damage function so that climate change can affect growth. We take a canonical endogenous growth model and modify it so that climate change has an impact on the sectors in the economy that contribute to growth. For example, if an economy is modeled as having an inventing sector and a manufacturing sector, we allow climate damages to affect the output of both sectors. With this simple reformulation, the effects of climate change can be dramatically different than in standard models. Over reasonably long periods, the growth effects dominate, showing that studying the impacts of climate change on growth should be of central concern.

Our goal is to consider this effect in all of the canonical endogenous growth models to see how robust the effect is across model choices. We have solved two models so far and likely will want to consider another two or three. In addition, we are simulating the results of one or more models to understand the likely size of the effects under differing parameter choices.


David WeisbachMishung Ahn


Model Uncertainty and Energy Technology Policy

Energy modeling, numerical modeling based on economic principles, has become the dominant analytical tool in U.S. energy policy. Energy models are widely used by researchers across the public and private sectors. However, the widespread application of these models in policy analysis poses challenges to decision-makers. We are developing a framework and analysis that demonstrate how non-Bayesian decision rules can address fundamental model uncertainty in the domain of energy policy, technological change, and greenhouse gas abatement.

Numerical modeling based on economic principles has become the dominant analytical tool in U.S. energy policy. Energy models are now used extensively by public agencies, private entities, and academic researchers, and in recent years have also formed the core of integrated assessment models used to analyze the relationships among the energy system, the economy, and the global climate. However, fundamental uncertainties are intrinsic in what has become the typical circumstance of multiple models embodying different representations of the energy-economy, and producing different policy-relevant outputs that model users are compelled to interpret as equally plausible and/or valid. Because the policy implications of these outputs can diverge substantially, policy-makers are confronted with a significant degree of model-based uncertainty and little or no guidance as to how it should be addressed.

This problem of “model uncertainty” has recently been the focus of work in macroeconomics, where scholars have studied the problem of how a decision-maker should proceed in the face of uncertainty re- garding the correct model of an economic system that is the object of policy. We focus on analyzing a low-dimensional numerical integrated assessment model using the “minimax regret” metric. Specifically, we have demonstrated that deep uncertainty regarding energy-related technological change can be addressed using this approach. Our findings include comparison with expected cost minimization, to show how the interaction of solution methods and model structure affect the influence of this form of deep uncertainty on low-run CO2 emissions abatement policies. We also examined other methods assuming some prior distribu- tions over uncertain parameters for analyzing the difference between our robust solution and the non-robust solution from those methods.

We demonstrate that the fundamental model uncertainty can be represented and analyzed in the context of energy policy problems determining optimal CO2 abatement strategies. The robust solution from min-max regret method is significantly different with any solutions from sensitivity analysis over uncertain parameters or those methods assuming prior distributions over uncertain parameters. The following figure shows the difference of the robust min-max regret solution over all three uncertain parameters (the red line) and others with min-max regret solution over only one uncertain parameter, Technical Change level, while the other two parameters are used for sensitivity analysis. 

Abatement paths in computational model of min-max regret criterion with three uncertain parameters, and sensitivity analysis of min-max regret criterion with only one uncertain parameter

Abatement paths in computational model of min-max regret criterion with three uncertain parameters, and sensitivity analysis of min-max regret criterion with only one uncertain parameter



Yongyang Cai | Alan Sanstad

Alumni: Kenneth Judd 

Recent Publications

Illinois renewable portfolio standards

Recently enacted state Renewable Portfolio Standards (RPSs) collectively require that U.S. electricity generation by non-hydro renewables more than double by 2025. These goals are not certain to be met, however, because many RPSs apply cost caps that alter requirements if costs exceed targets. We have analyzed the 2008 Illinois RPS, which is fairly typical, and have found that at current electricity prices, complete implementation will require significant decreases in renewables costs, even given the continuation of federal renewables subsidies. While full implementation is possible, it is not assured.

We also find that the statutory design raises additional concerns about unintended potential consequences. First, the fact that wind power and solar carve-outs fall under a single cost cap leaves each technology vulnerable to the economics of the other. In failure mode, a less cost-effective technology can curtail deployment of a more cost-effective one. Second, adjacent-state provisions mean the bulk of the wind power requirement under the Illinois RPS can be met by existing facilities in Iowa, where new builds will likely also occur. We conclude that the Illinois RPS, and likely those of many other states, appear to combine objectives inherently in conflict and whose conflicts can create legislative failure: preferences for local jobs, for specific technolo- gies, for environmental benefits, and for low costs. Since RPSs are the principal policy mechanisms in the U.S. at present for combating climate change, it is important to revisiting existing legislation if necessary to ensure legislative success. The Illinois analysis can provide an example and guidelines for other states that will face similar pressure on their RPSs in the near future.

Click here to visit the RPS Calculator.


Elisabeth Moyer | Alison BriziusSean Johnson | Lexi Goldberger | Joe Zhu

Recent Publications:

RPS Calculator

The major policy instruments for mitigating climate change actually in use in the U.S. are subsidies provided to renewable energy. A popular means of subsidy is through state-level Renewable Portfolio Standards (RPSs), requirements enacted by many states that require a certain fraction of electricity must be derived from renewables. However, many state RPSs are infeasible because of “cost cap” provisions that do not permit renewables to be sufficiently competitive, and feasibility is generally not assessed before legislation is passed. The RPS calculator allows the user to explore the conditions for RPS success or failure in different states. The user can analyze and modify existing state statutes or design new statutes in states that do not have them. Users can explore the effects of parameters such as electricity prices, generation costs for wind and solar, interest rates, technology carveouts, and cost cap structure. Features under development include extension to all U.S. states (currently only IL and CA) and spatial variation in wind speed (wind capacity factor map).


Current: Elisabeth Moyer

Alumni: Sean Johnson | Lexi Goldberger | Joe Zhu

Recent Publications

Confronting the Food-Energy-Environment Trilemma

The allocation of the world’s land resources over the course of the coming century has become a pressing research question. Continuing population increases, improving, land-intensive diets among the poorest populations in the world, increasing production of biofuels and rapid urbanization in developing countries are all competing for land even as the world looks to land resources to supply more environmental services. The latter include biodiversity and natural lands, as well as forests and grasslands devoted to carbon sequestration. And all of this is taking place in the context of faster than expected climate change which is altering the biophysical environment for land-related activities. This combination of intense competition for land, coupled with highly uncertain future productivities and valuations of environmental services, gives rise to a significant problem of decision making under uncertainty. The issue is compounded by the inherent irreversibility of many land use decisions.

The goal of this study is to determine the optimal profile for global land use in the context of growing commercial demands for food and forest products, increasing non-market demands for ecosystem services, and more stringent greenhouse gas (GHG) mitigation targets. We do so by developing a new model, nick-named FABLE: forest, agriculture, and biofuels in a land use model with environmental services. This model determines the optimal allocation of scarce land, both across competing uses as well as across time. While market failures, including ill-defined property rights, poorly developed land markets, lack of information, and credit constraints preclude such a path from being achieved in reality, this optimal path is a useful point of reference for those seeking to influence patterns of global land use. The resulting long-run, forward-looking, computable partial equilibrium model, covers key sectors drawing on the world’s land resources, and incorporates growing demands for food, renewable energy, and forest products, as well as non-market demands for ecosystem services. We also consider alternative GHG constraints, as well as the potential impacts of climate change itself on the productivity of land in agriculture, forestry and ecosystem services.


Our baseline reflects developments in global land use over the 10 years that have already transpired, while also incorporating long-run projections of population, income and demand growth from a variety of international agencies. The model baseline suggests that, even in the absence of GHG regulations, deforestation rates associated with cropland expansion decline along the optimal land-use trajectory in the medium term. This is important, since deforestation accounts for a large share of current global GHG emissions. In the long term there is a significant expansion of the livestock sector, driven by increasing per capita incomes, and this is fueled by increasingly intensive production practices. The area of protected natural lands, which deliver valuable ecosystem services, also increases strongly in the long run. However, this finding is sensitive to the choice of social discount rate. A higher rate of discount results in a sacrifice of forest cover and ecosystem services in favor of more immediate delivery of services from food and energy consumption. Along the baseline, the consumption of biofuels increases rapidly after second generation biofuels become commercially viable in 2035, and provides for about a third of total liquid fuel consumption by the end of this century, along the optimal path under our baseline scenario.

We consider three counterfactual scenarios aimed at capturing the most important sources of uncertainty associated with this long run trajectory for global land use, climate impacts on agriculture, energy prices, and global GHG emissions regulations.

  • Adverse climate impacts on crop yields curtail food production, requiring additional cropland and encouraging additional fertilizer use, thereby leading to higher GHG emissions.
  • Energy prices affect the optimal deforestation rate as well as the overall amount of land used in agriculture.
    • By mid-century, cropland area increases sharply under higher energy prices, due to the incentive for increased biofuel production as well as higher fertilizer prices which raise the cost of intensification.
    • Substantially more deforestation occurs under this scenario and the increased GHG emissions from land use change outweigh the emissions fall from displacement of petroleum consumption by biofuels and declining fertilizer use.
  • When we also require the world’s land base to deliver land-based GHG abatement, the pressure on global natural land resources becomes even more significant.
    • While the introduction of the land based GHG emissions constraint leads to a significant reduction in GHG emission flows over the twentyfirst century, its effectiveness is eroded by a substantial increase in GHG emissions after the policy is announced, but before the policy is actually implemented.
    • This mimics the ‘green paradox’ found in other areas of environmental regulation. Since such pre- announcement seems inevitable from a political-economic perspective, it is an issue which deserves greater attention. Indeed, we find a leakage rate of 56%, which is very high and threatens to undo most of the GHG mitigation benefits of such a policy.

When all three ‘scenarios’ are simultaneously realized, the world’s land resources face a ‘perfect storm’ in which the cost of agricultural intensification is higher, biofuels expand their area, additional cropland is needed to offset the adverse impacts of climate change, and climate regulation also places new pressures on land availability for food. In this case the optimal path of food consumption is significantly lower, highlighting the potential for intense competition for land in the production of the world’s food, fuel and environmental services over the twentyfirst century. 


Jevgenijs Steinbuks | Thomas W. Hertel

Recent Publications: